Search Results (20)

Aviation security increasingly relies on explosive trace detectors (ETDs), particularly those employing ion mobility spectrometry (IMS). However, few studies have systematically compared the performance of IMS-based ETDs, especially in terms of measurement uncertainty and stability under repeated operation. This study evaluated two commercially available IMS-based ETDs using statistical analysis and data visualization. Repeated TNT (2,4,6-trinitrotoluene) detection tests were conducted to assess performance over consecutive operations. The results revealed significant differences in measurement uncertainty between the two devices. One ETD exhibited stable measurements throughout, while the other showed variance fluctuations that stabilized only after extended use. Despite using the same detection principle, the two devices responded differently to operational conditions, suggesting that internal specifications and design choices significantly affect reliability. This study offers a methodological framework for ETD comparison and provides insights to support more rigorous evaluation and certification practices in aviation security. Full article

Pulmonary arterial hypertension (PAH) is a chronic and progressive disease marked by vascular remodeling, inflammation, and smooth muscle cell proliferation, with limited treatment options focused primarily on symptom management. The multifactorial nature of PAH, encompassing genetic, autoimmune, and connective tissue contributions, complicates its treatment, while irreversible vascular changes, such as fibrosis, remain unaddressed by current therapies. Fundamental research on molecular pathways and targeted delivery systems has paved the way for advanced therapeutic strategies that aim to modify disease progression rather than merely manage symptoms. Nanoparticle-based drug delivery systems, leveraging controlled release and pulmonary targeting, offer a promising avenue to overcome these challenges. Such systems enable precise localization to pulmonary vasculature, minimize systemic side effects, and support emerging approaches like gene therapy and combination treatments. Future research should focus on refining nanoparticle formulations for personalized medicine, optimizing inhalation delivery systems, and integrating multi-target approaches to achieve curative outcomes in PAH. This review explores pathophysiology of PAH, current pharmacological strategies, and innovative nanoparticle-based therapies, emphasizing their potential to transform PAH treatment and address its underlying mechanisms. Full article

Atopic dermatitis (AD) is a chronic, inflammatory skin condition characterized by severe pruritus and recurrent flare-ups, significantly impacting patients’ quality of life. Current treatments, such as corticosteroids and immunomodulators, often provide symptomatic relief but can lead to adverse effects with prolonged use. Seaweed, a sustainable and nutrient-dense resource, has emerged as a promising alternative due to its rich bioactive compounds—polysaccharides, phlorotannins, polyphenols, and chlorophyll—that offer anti-inflammatory, antioxidant, and immunomodulatory properties. This review explores the therapeutic potential of brown, red, and green algae in alleviating AD symptoms, highlighting the effects of specific species, including Undaria pinnatifida, Laminaria japonica, Chlorella vulgaris, and Sargassum horneri. These seaweeds modulate immune responses, reduce epidermal thickness, and restore skin barrier function, presenting a novel, safe, and effective approach to AD management. Further clinical studies are needed to confirm their efficacy and establish dosing strategies, paving the way for seaweed-derived therapies as natural alternatives in AD treatment. Full article

Melanogenesis, the biological process responsible for melanin synthesis, plays a crucial role in determining skin and hair color, photoprotection, and serving as a biomarker in various diseases. While various factors regulate melanogenesis, the role of fatty acids in this process remains underexplored. This study investigated the anti-melanogenic properties of 10(E)-pentadecenoic acid (10E-PDA) through both in silico and in vitro analyses. SwissSimilarity was utilized to predict the functional properties of 10E-PDA by comparing it with structurally similar lipids known to exhibit anti-melanogenic effects. Subsequent in vitro experiments demonstrated that 10E-PDA significantly reduced melanin production and intracellular tyrosinase activity in α-MSH (melanocyte-stimulating hormone)-stimulated B16F10 melanoma cells without exhibiting significant cytotoxicity at concentrations up to 15 μM. Further mechanistic studies revealed that 10E-PDA inhibited the nuclear translocation of microphthalmia-associated transcription factor (MITF), consistent with the decrease observed in p-MITF protein levels. It also decreased the mRNA levels of tyrosinase-related proteins (TRP-1, TRP-2) and tyrosinase, while reducing the protein levels of TRP-1 and tyrosinase, but not TRP-2. These findings suggest that 10E-PDA exerts its anti-melanogenic effects by modulating the MITF/tyrosinase axis, presenting potential therapeutic implications for skin pigmentation disorders. Full article

Melanogenesis, essential for skin photoprotection and pigmentation, can lead to disorders like melasma and hyperpigmentation, which are challenging to treat and affect quality of life. Docosatrienoic acid (DTA), a polyunsaturated omega-3 fatty acid, has been identified as a potential regulator of skin aging. This study investigates DTA’s effects on melanogenesis and its underlying molecular mechanisms using in silico and in vitro analyses. SwissSimilarity analysis revealed that DTA shares close structural similarities with known anti-melanogenic lipids, suggesting it may inhibit melanogenesis in similar manners. Our results demonstrated that DTA reduces melanin content and intracellular tyrosinase activity in B16F10 cells, significantly downregulating the mRNA expression of tyrosinase, TRP-1, and TRP-2 by inhibiting MITF translocation to the nucleus. While DTA exhibited mild inhibitory effects on mushroom tyrosinase activity and antioxidant properties at higher concentrations, direct inhibition of tyrosinase is likely not the primary mechanism, as the observed anti-melanogenic effects occurred at much lower concentrations compared to those required for direct tyrosinase inhibition. Together, DTA-mediated modulation of MITF and tyrosinase mRNA expression offers a novel approach to treating hyperpigmentation. DTA’s potential extends into the cosmetic industry, enhancing product stability, functionality, and aesthetics. Further research is needed to explore DTA’s broader applications in skincare and cosmetic formulations. Full article

This study examines the potential of religious facilities to enhance urban tourism by evaluating urban traditional temples as cultural tourism resources and identifying key tourism indicators. An evaluation framework was developed, encompassing five attributes—historicity, accessibility, inter-connectivity, convenience, and publicity—each with three sub-factors. The research question focuses on what factors distinguish successful urban traditional temples like Bongeunsa and Jogyesa from others. Seven traditional temples in Seoul were selected for comparison. Bongeunsa and Jogyesa, functioning as major tourist attractions, were compared with five other temples with potential but fewer visitors. The findings indicate that Bongeunsa and Jogyesa scored higher for all of the attraction attributes and the majority of the sub-factors. Differences in the correlation between each factor and visitor numbers were also observed. The research methodology included literature reviews, deriving cultural tourism attributes, and evaluating selected temples. This approach highlights underdeveloped factors in other urban temples and suggests strategies for enhancement, providing valuable insights for urban tourism policy and development. Full article

Disposable wearable sensors that ultrathin and conformable to the skin are of significant interest as affordable and easy-to-use devices for short-term recording. This study presents a facile and low-cost method for transferring spray-coated silver nanowire (AgNW) composite films onto human skin using glossy paper (GP) and liquid bandages (LB). Due to the moderately hydrophobic and rough surface of the GP, the ultrathin AgNWs composite film (~200 nm) was easily transferred onto human skin. The AgNW composite films conformally attached to the skin when applied with a LB, resulting in the stable and continuous recording of wearable electrophysiological signals, including electromyogram (EMG), electrocardiogram (ECG), and electrooculogram (EOG). The volatile LB, deposited on the skin via spray coating, promoted rapid adhesion of the transferred AgNW composite films, ensuring stability to the AgNWs in external environments. The AgNWs composite supported with the LB film exhibited high water vapor breathability (~28 gm⁻²h⁻¹), which can avoid the accumulation of sweat at the skin–sensor interface. This approach facilitates the creation of rapid, low-cost, and disposable tattoo-like sensors that are practical for extended use. Full article

Lymph nodes are organs that control immune cells and provide a major pathway for primary tumors to metastasize. A nanoparticles-based strategy has several advantages that make it suitable for achieving effective lymphatic delivery. First, the size of nanoparticles can be tailored to meet a size range appropriate for lymphatic migration. In addition, functionalized nanoparticles can target cells of interest for delivery of drugs or imaging probes. Existing lymph node contrast agents map all lymph nodes regardless of metastasis status; however, by using nanoparticles, it is possible to selectively target lymphatic metastases. Moreover, using functionalized nanoparticles, it is possible to specifically deliver anticancer drugs to metastatic lymph nodes. In this review, we introduce the use of nanoparticles for lymphatic mapping, in particular highlighting design considerations for detecting metastatic lymph nodes. Furthermore, we assess trends in lymph node-targeting nanoparticles in clinical practice and suggest future directions for lymph node-targeting nanoparticles. Full article

Acid-reducing agents are commonly used for the prevention of aspirin-induced gastrointestinal complications such as peptic ulcers. As a novel potassium-competitive acid blocker, fexuprazan is expected to prevent aspirin-induced gastrointestinal complications. This randomized, open-label study aimed to evaluate the pharmacodynamic and pharmacokinetic interactions between aspirin and fexuprazan in healthy Koreans. Subjects randomized to the aspirin group received 500 mg aspirin in combination with 80 mg fexuprazan. For the fexuprazan group, fexuprazan 80 mg was administered alone and then in combination with aspirin 500 mg. Platelet aggregation inhibited by aspirin and the pharmacokinetic parameters of aspirin and fexuprazan were compared between monotherapy and combination therapy. A total of 22 subjects completed the study. The platelet aggregation-inhibitory activity and systemic exposure to aspirin were not significantly affected by fexuprazan coadministration. The systemic exposure of fexuprazan was decreased up to 20% by aspirin coadministration, which was not regarded as clinically meaningful considering the previously reported exposure–response relationship. In conclusion, there were no clinically relevant pharmacodynamic or pharmacokinetic interactions between aspirin and fexuprazan. This finding suggests the potential of fexuprazan for the prevention of aspirin-induced gastrointestinal complications, serving as a baseline for optimizing its therapeutic application with aspirin. Full article

Locating cracks in a solid object using acoustic emission (AE) is useful both for detecting defects during safety monitoring and for basic laboratory studies of fractures. We developed an acoustic source location (ASL) method without the use of velocity information with AE in anisotropics plates, such as carbon fiber-reinforced polymers. Assuming that the propagation velocity of an unknown elastic wave is constant in anisotropic materials, the objective function to be minimized is defined based on the elliptic wavefront shape-based technique. The objective function is minimized using an iterative method, such as the gradient descent method. As a result of the numerical experiments and PLB testing on a carbon fiber-reinforced polymer plate, the method is accurate within 5% and is stable against noise. Full article

Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven. Full article

Amikacin is used as a therapy for patients with nontuberculous mycobacterial pulmonary disease (NTM-PD) who are resistant to macrolide antibiotics or have severe symptoms. This study aimed to characterize the pharmacokinetic properties of amikacin in patients with NTM-PD by developing a population pharmacokinetic model and to explore the optimal pharmacotherapy in patients with NTM-PD. For this study, all data were retrospectively collected. The amikacin pharmacokinetic properties were best described by a two-compartment model with first-order elimination. The estimated glomerular filtration rate and body weight were identified as significant covariates for clearance and the volume of distribution, respectively. A model-based simulation was conducted to explore the probability of reaching the target therapeutic range when various dose regimens were administered according to the body weight and renal function. The simulation results indicated that the amikacin dosage should be determined based on the body weight, and for patients who weigh over 70 kg, it is necessary to adjust the dose according to renal function. In conclusion, the optimal pharmacotherapy of amikacin for patients with NTM-PD was recommended based on the population pharmacokinetic model, which is expected to enable the personalization of drug therapy and improve the clinical outcomes of amikacin therapy. Full article

Toxic herbs are similar in appearance to those known to be safe, which can lead to medical accidents caused by identification errors. We aimed to study the deep learning models that can be used to distinguish the herb Aristolochiae Manshuriensis Caulis (AMC), which contains carcinogenic and nephrotoxic ingredients from Akebiae Caulis (AC) and Sinomenium acutum (SA). Five hundred images of each herb without backgrounds, captured with smartphones, and 100 images from the Internet were used as learning materials. The study employed the deep-learning models VGGNet16, ResNet50, and MobileNet for the identification. Two additional techniques were tried to enhance the accuracy of the models. One was extracting the edges from the images of the herbs using canny edge detection (CED) and the other was applying transfer learning (TL) to each model. In addition, the sensitivity and specificity of AMC, AC, and SA identification were assessed by experts with a Ph.D. degree in herbology, undergraduates and clinicians of oriental medicine, and the ability was compared with those of MobileNet-TL′s. The identification accuracies of VGGNet16, ResNet50, and MobileNet were 93.9%, 92.2%, and 95.6%, respectively. After adopting the CED technique, the accuracy was 95.0% for VGGNet16, 63.9% for ResNet50, and 80.0% for MobileNet. After using TL without the CED technique, the accuracy was 97.8% for VGGNet16-TL, 98.9% for ResNet50-TL, and 99.4% for MobileNet-TL. Finally, MobileNet-TL showed the highest accuracy among three models. MobileNet-TL had higher identification accuracy than experts with a Ph.D. degree in herbology in Korea. The result identifying AMC, AC, and SA in MobileNet-TL has demonstrated a great capability to distinguish those three herbs beyond human identification accuracy. This study indicates that the deep-learning model can be used for herb identification. Full article

This paper describes a fourth-order cascade-of-integrators with feedforward (CIFF) single-bit discrete-time (DT) switched-capacitor (SC) delta-sigma modulator (DSM) for high-resolution applications. This DSM is suitable for high-resolution applications at low frequency using a high-order modulator structure. The proposed operational transconductance amplifier (OTA), used a feedforward amplifier scheme that provided a high-power efficiency, a wider bandwidth, and a higher DC gain compared to recent designs. A chopper-stabilization technique was applied to the first integrator to remove the 1/f noise from the transistor, which is inversely proportional to the frequency. The designed DSM was implemented using 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The oversampling ratio (OSR) was 128, and the sampling frequency was 128 kHz. At a 500 Hz bandwidth, the signal-to-noise ratio (SNR) was 100.3 dB, the signal-to-noise distortion ratio (SNDR) was 98.5 dB, and the dynamic range (DR) was 103 dB. The measured total power dissipation was 99 µW from a 3.3 V supply voltage. Full article

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms. Full article